Spatio-Spectroscopic Representation Learning using Unsupervised Convolutional Long-Short Term Memory Networks
This work addresses the challenge of analyzing complex IFS data for astronomers, but it is incremental as it applies a hybrid deep learning method to a new dataset without establishing broad SOTA.
The authors tackled the problem of learning spatio-spectroscopic representations from Integral Field Spectroscopy (IFS) surveys to uncover insights into galaxy evolution, by developing an unsupervised deep learning framework using Convolutional LSTM Autoencoders on a sample of ~9000 galaxies and demonstrating it on 290 Active Galactic Nuclei (AGN) to highlight anomalous characteristics.
Integral Field Spectroscopy (IFS) surveys offer a unique new landscape in which to learn in both spatial and spectroscopic dimensions and could help uncover previously unknown insights into galaxy evolution. In this work, we demonstrate a new unsupervised deep learning framework using Convolutional Long-Short Term Memory Network Autoencoders to encode generalized feature representations across both spatial and spectroscopic dimensions spanning $19$ optical emission lines (3800A $< λ<$ 8000A) among a sample of $\sim 9000$ galaxies from the MaNGA IFS survey. As a demonstrative exercise, we assess our model on a sample of $290$ Active Galactic Nuclei (AGN) and highlight scientifically interesting characteristics of some highly anomalous AGN.